Abstract
This study presents a method for selecting and combining feature models constructed by the machine learning on the processing task capability. The evaluation of combining the feature models shows that the processing task capability can be improved by selecting and reaching feature models based on their similarity to the vector of queries without combining all feature models. Then, we discuss a method for constructing logical the R-Tree algorithm on the distributed fog nodes. For future work, we will implement the proposed method on various types of data.
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Acknowledgement
This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in Aid for Scientific Research (C), 2021–2023 21K11850, Takeshi TSUCHIYA.
Work by Tran Minh Quang acknowledges the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM.
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Tsuchiya, T., Mochizuki, R., Hirose, H., Yamada, T., Koyanagi, K., Minh, Q.T. (2021). Selective Combination and Management of Distributed Machine Learning Models. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_8
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DOI: https://doi.org/10.1007/978-3-030-91387-8_8
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